Visit my Google Scholar page here for a complete list of publications. * indicates a student working under my (co)supervision.

Data Integration/divide-and-conquer

  • E.C. Hector and P.X.-K. Song (2021). A distributed and integrated method of moments for high-dimensional correlated data analysis. Journal of the American Statistical Association 116(534):805-818. Paper, code.
  • E.C. Hector and P.X.-K. Song (2020). Doubly distributed supervised learning and inference with high-dimensional correlated outcomes. Journal of Machine Learning Research 21:1–35. Paper, code.
  • E.C. Hector and P.X.-K. Song (2022). Joint integrative analysis of multiple data sources with correlated vector outcomes. The Annals of Applied Statistics 16(3):1700-1717. Paper, code.
  • ​E.C. Hector, L. Luo and P.X.-K. Song (2022). Parallel-and-stream accelerator for computationally fast supervised learning. Computational Statistics and Data Analysis 177:107587. Paper.
  • E.C. Hector (2020+). Fused mean structure learning in data integration with dependence. In review.
  • E.C. Hector and B.J. Reich (2022+). Distributed inference for spatial extremes modeling in high dimensions. arXiv, arXiv:2204.14165. Paper. In review.
  • E.C. Hector and R. Martin (2022+). Turning the information-sharing dial: efficient inference from different data sources. arXiv, arXiv:2207.08886. Paper. In review.

Wearable Devices

  • E.C. Jansen, E.C. Hector, J.M. Goodrich, A. Cantoral, M.M. Téllez Rojo, N. Basu, P.X.-K. Song, L. Torres Olascoaga, K.E. Peterson (2020). Mercury exposure in relation to sleep duration, timing, and fragmentation among adolescents in Mexico City. Environmental Research 191: 110216. doi: j.envres.2020.110216. Paper.
  • L. Shi*, M. Wank*, Y. Chen*, Y. Wang*, Y. Liu, E.C. Hector, P.X.-K. Song (2022). Sleep Classification with Artificial Synthetic Imaging Data from Empatica E4 Wristband by Convolutional Neural Networks. To appear in IEEE Journal of Biomedical and Health Informatics.
  • L. Luo, J. Wang and E.C. Hector (2021+). Statistical inference for streamed longitudinal data. arXiv, arXiv:2208.02890. Paper. In review.
  • C. Manschot* and E.C. Hector (2022+). Functional regression with intensively measured longitudinal outcomes: a new lens through data partitioning. arXiv, arXiv:2207.13014. Paper. In review.

Metabolomics

  • X. Yin, L.S. Chan, D. Bose, A.U. Jackson, P. VandeHaar, A.E. Locke, C. Fuchsberger, H.M. Stringham, K. Yu, L. Fernandes Silva, D. Zhang, E.C. Hector, E. Young, L. Ganel, I. Das, H.J. Abel, M.R. Erdos, L.L. Bonnycastle, J. Kuusisto, N.O. Stitziel, I. Hall, G.R. Wagner, J. Kang, J. Morrison, C.F. Burant, F.S. Collins, S. Ripatti, A. Palotie, N.B. Freimer, K.L. Mohlke, L. Scott, X. Wen, E. Fauman, M. Laakso, M. Boehnke (2022). Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nature Communications, doi: 10.1038/s41467-022-29143-5. Paper.
  • J.M. Goodrich, E.C. Hector, L. Tang, J.L. LaBarre, D.C. Dolinoy, A. Mercado-Garcia, A. Cantoral, P.X.-K. Song, M.M. Téllez-Rojo and K.E. Peterson (2020). Integrative analysis of gene-specific DNA methylation and untargeted metabolomics data from the ELEMENT cohort. Epigenetic Insights 13:1-10. doi: 10.1177/2516865720977888. Paper.
  • W. Perng, E.C. Hector, P.X.-K. Song, M.M. Téllez Rojo, S. Raskind, M. Kachman, A. Cantoral, B.F. Burant and K.E. Peterson (2017). Metabolomic determinants of metabolic risk in Mexican adolescents. Obesity (Silver Spring) . Paper.